A quality modeling of Ginjo sake using neural network (NN) and genetic algorithm (GA) was constructed. In order to estimate 7 sensory evaluations on the quality of Ginjo sake from 18analytical values on chemical component, NN model was successfully applied. Fuzzy NN (FNN) and hierarchical fuzzy NN (HFNN) was found to estimate these relations more precisely than NN.Using this model, the analytical data on chemical component was estimated from 7 given values on sensory evaluation by means of GA as an optimizing method. It was found that almost all estimated values coincided with the actual values in the range of the error of less than 0.3.In order to estimate the cell concentration of koji fungi and four enzymatic activities on rice koji, image analysis was applid. This method was found to be applicable to monitor the growth in koji making process.To control temperature of Ginjo moromi fermentation automatically, experimental fermentaitions (10,100 and 1000 kg total rice) based on fuzzy neural network (FNN) were carried out. The concentrations of chemical components, physical properties, concentrations of flavor components and sensory evaluation had almost the same values of the manual control of a Toji, suggesting that Ginjo sake can be made under FNN control.